• The Journal of the Korea Contents Association
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• v.5 no.3
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• pp.29-35
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• 2005

The task for chromosome analysis and diagnosis by experienced cytogenetists are being concerned as repetitive, time consuming job and expensive. For that reason, intelligent agent based on chromosome knowledge base using web has been developed to be able to analyze chromosomes and obtain necessary advises from the knowledge base instead of human experts. That is to say, the knowledge base of IF THEN production rule was implemented to a knowledge domain with normal and abnormal chromosomes, and then the inference results by the knowledge base could enter the inference data into the database. Experimental data were composed of normal chromosomes of 2,736 cases and abnormal chromosomes of 259 cases that have been obtained from GTG-banding metaphase peripheral blood and amniotic fluid samples. The completed intelligent agent for the chromosome knowledge base provides variously morphological information by analysis of normal or abnormal chromosomes also has the advantage of being able to consult with the user on the chromosome analysis and diagnosis.

• Journal of the Korean Institute of Intelligent Systems
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• v.1 no.1
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• pp.9-25
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• 1991

This tutorial paper has been written for biologists, physicians or beginners in fuzzy sets theory and applications. This field is introduced in the framework of medical diagnosis problems. The paper describes and illustrates with practical examples, a general methodology of special interest in the processing of borderline cases, that allows a graded assignment of diagnoses to patients. A pattern of medical knowledge consists of a tableau with linguistic entries or of fuzzy propositions. Relationships between symptoms and diagnoses are interpreted as labels of fuzzy sets. It is shown how possibility measures (soft matching) can be used and combined to derive diagnoses after measurements on collected data. The concepts and methods are illustrated in a biomedical application on inflammatory protein variations. In the case of poor diagnostic classifications, it is introduced appropriate ponderations, acting on the characterizations of proteins, in order to decrease their relative influence. As a consequence, when pattern matching is achieved, the final ranking of inflammatory syndromes assigned to a given patient might change to better fit the actual classification. Defuzzification of results (i.e. diagnostic groups assigned to patients) is performed as a non fuzzy sets partition issued from a "separating power", and not as the center of gravity method commonly employed in fuzzy control. It is then introduced a model of fuzzy connectionist expert system, in which an artificial neural network is designed to build the knowledge base of an expert system, from training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the connections: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through MIN-MAX fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feed forward network is described and illustrated in the same biomedical domain as in the first part.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.349-354
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• 2005

In this paper, we propose a mobile telemedicine system that acquires more easily and analyzes individual's bio-signal using PDA. It is not easy for modern people who live busily, disabled patients, or old people to visit hospital. The major goal of this study is to implement the mobile telemedicine systems that the captured bio-signal from remote hospital or other medical treatment device is transmitted via Bluetooth module in ubiquitous environment, PDA with built-in Bluetooth module receives its data and displays on the screen in various form. By implemented systems, it is possible to compare current bio-signal with historical bio-signal and analyze bio-signal, and it is able to make a self diagnosis and it is available to be examined and treated remote diagnosis by sending stored bio-signal to a medical doctor.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.3
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• pp.341-346
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• 2012

In rehabilitation where ultrasonographic diagnosis is not popular, it could be subjective by medical expert's experience. Thus, it is necessary to develop an objective automative procedure in ultrasonic image analysis. A disadvantage of existing automative analytic procedure in musculoskeletal system is to designate an incorrect muscle area when the figure of fascia is vague. In this study, we propose a new procedure to extract more accurate muscle area in abdomen ultrasonic image for that purpose. After removing unnecessary noise from input image, we apply End-in Search algorithm to enhance the contrast between fascia and muscle area. Then after extracting initial muscle area by Up-Down search, we trace the fascia area with a mask based on morphological and directional information. By this tracing of mask movements, we can emphasize the fascia area to extract more accurate muscle area in result. This new procedure is proven to be more effective than existing methods in experiment using convex ultrasound images that are used in real world rehabilitation diagnosis.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.718-723
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• 2007

In this paper, we developed a personal hypertension management system (PHMS) having self-diagnosis function with PDA. The developed PHMS consists of five modules such as a personal information management, a life management, a food management, a sickness management, and network management modules. The personal information management module offers physical and fatness information as well as personal information. The life management module gives exercise and body mass index. The food management module includes caloric intake and the sickness management module renders a personal blood pressure and a subjective symptom. Finally, wireless networks are implemented for the network management. From these, it is possible to make a self diagnosis and be examined and treated remotely by sending the stored blood pressure related information to a medical doctor.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.148-162
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• 2023

Classification systems can significantly assist the medical sector by allowing for the precise and quick diagnosis of diseases. As a result, both doctors and patients will save time. A possible way for identifying risk variables is to use machine learning algorithms. Non-surgical technologies, such as machine learning, are trustworthy and effective in categorizing healthy and heart-disease patients, and they save time and effort. The goal of this study is to create a medical intelligent decision support system based on machine learning for the diagnosis of heart disease. We have used a mixed feature creation (MFC) technique to generate new features from the UCI Cleveland Cardiology dataset. We select the most suitable features by using Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination with Random Forest feature selection (RFE-RF) and the best features of both LASSO RFE-RF (BLR) techniques. Cross-validated and grid-search methods are used to optimize the parameters of the estimator used in applying these algorithms. and classifier performance assessment metrics including classification accuracy, specificity, sensitivity, precision, and F1-Score, of each classification model, along with execution time and RMSE the results are presented independently for comparison. Our proposed work finds the best potential outcome across all available prediction models and improves the system's performance, allowing physicians to diagnose heart patients more accurately.

• The Transactions of the Korea Information Processing Society
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• v.7 no.8
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• pp.2497-2504
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• 2000

This paper is aimed at defining items of fault, and then constructing rules of fault diagnosis and recovery using INBANCA technology for the purpose of managing the weh server. The fault items of web server consist of the process fault, server overload, network interface fault, configuration and performance fault. Based on these items, the actual fault management is carried out fault referencing. In order to reference the fault, we have formulated the system-level fault diagnosis production rule and the service-level fault diagnosis rule, conjunction with translating management knowledge into active network. Also, adaptive recovery mechanism of web server is applied to defining recovery rule and constructing case library for case-based web server fault recovery. Finally, through the experiment, fault environment and applicability of each proposed production rule and recovering scheme are presented to verify justification of proposed diagnosis rules and recovery mechanism for fault management. An intelligent case-based fault management scheme proposed in this paper can minimize an effort of web master to remove fault incurred web administration and operation.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.2
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• pp.135-140
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• 2016

Corrective controllers enable fault diagnosis and tolerance for various faults in asynchronous sequential circuits without resort to redesign. In this paper, we propose a static corrective controller in order to decrease the size of the controller. Compared with dynamic controllers, static controllers can be made using only combinational circuits, as they need no inner states. We address the existence condition and design procedures for static corrective controllers that overcome state transition faults. To show the validity and advantage, the proposed controller is applied to an SEU error counter implemented on FPGA.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.238-245
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• 2016

Inverters are considered the basic building blocks of industrial electrical drive systems that are widely used for various applications; however, the failure of electronic switches mainly affects the constancy of these inverters. For safe and reliable operation of an electrical drive system, faults in power electronic switches must be detected by an efficient system that is capable of identifying the type of faults. In this paper, an open switch fault identification technique for a three-phase inverter is presented. Single, double, and triple switching faults can be diagnosed using this method. The detection mechanism is based on stator current analysis. Discrete wavelet transform (DWT) using Daubechies is performed on the Clarke transformed (-) stator current and features are extracted from the wavelets. An artificial neural network is then used for the detection and identification of faults. To prove the feasibility of this method, a Simulink model of the DWT-based feature extraction scheme using a neural network for the proposed fault detection system in a three-phase inverter with an induction motor is briefly discussed with simulation results. The simulation results show that the designed system can detect faults quite efficiently, with the ability to differentiate between single and multiple switching faults.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.447-450
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• 2003

In this research, we propose an automatic knowledge acquisition and composite knowledge expression mechanism based on machine learning and relational database. Most of traditional approaches to develop a knowledge base and inference engine of expert systems were based on IF-THEN rules, AND-OR graph, Semantic networks, and Frame separately. However, there are some limitations such as automatic knowledge acquisition, complicate knowledge expression, expansibility of knowledge base, speed of inference, and hierarchies among rules. To overcome these limitations, many of researchers tried to develop an automatic knowledge acquisition, composite knowledge expression, and fast inference method. As a result, the adaptability of the expert systems was improved rapidly. Nonetheless, they didn't suggest a hybrid and generalized solution to support the entire process of development of expert systems. Our proposed mechanism has five advantages empirically. First, it could extract the specific domain knowledge from incomplete database based on machine learning algorithm. Second, this mechanism could reduce the number of rules efficiently according to the rule extraction mechanism used in machine learning. Third, our proposed mechanism could expand the knowledge base unlimitedly by using relational database. Fourth, the backward inference engine developed in this study, could manipulate the knowledge base stored in relational database rapidly. Therefore, the speed of inference is faster than traditional text -oriented inference mechanism. Fifth, our composite knowledge expression mechanism could reflect the traditional knowledge expression method such as IF-THEN rules, AND-OR graph, and Relationship matrix simultaneously. To validate the inference ability of our system, a real data set was adopted from a clinical diagnosis classifying the dermatology disease.